Fix SparseVector error for non-1D NumPy arrays

CHANGELOG.md

@@ -3,6 +3,7 @@
 - Fixed memory leak with `register_vector` and `register_vector_async` for Psycopg 3
 - Fixed error with `Bit` constructor and empty strings
 - Fixed equality for `HalfVector` with signed zeros
+- Fixed error type for multi-dimensional NumPy arrays with `SparseVector` constructor
 
 ## 0.5.1 (2026-10-08)
 

pgvector/sparsevec.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 from struct import pack, unpack_from
 from typing import TYPE_CHECKING, Final, cast, overload
-from ._utils import is_sparse_array, ndarray
+from ._utils import is_ndarray, is_sparse_array, ndarray
 
 if TYPE_CHECKING:
     import numpy as np
@@ -136,6 +136,9 @@ def _from_sparse(self, arr: sparray | spmatrix, /) -> None:
         self._values = [float(v[1]) for v in elements]
 
     def _from_dense(self, value: list[float] | ndarray, /) -> None:
+        if is_ndarray(value) and value.ndim != 1:
+            raise ValueError('expected ndim to be 1')
+
         self._dim = len(value)
         self._indices = []
         self._values = []

tests/test_sparse_vector.py

@@ -37,6 +37,15 @@ def test_init_ndarray(self) -> None:
         assert vec.to_list() == [1, 0, 2, 0, 3, 0]
         assert vec.indices() == [0, 2, 4]
 
+    def test_init_ndarray_ndim(self) -> None:
+        if np is None:
+            pytest.skip('NumPy required')
+
+        for arr in (np.zeros((2, 2)), np.array([[1], [0], [2]]), np.array(5.0)):
+            with pytest.raises(ValueError) as error:
+                SparseVector(arr)
+            assert str(error.value) == 'expected ndim to be 1'
+
     def test_init_dict(self) -> None:
         vec = SparseVector({2: 2, 4: 3, 0: 1, 3: 0}, 6)
         assert vec.to_list() == [1, 0, 2, 0, 3, 0]